I am broadly interested in how climate change impacts mountain aquatic ecosystems, specifically through the lens of aquatic invertebrates. At the moment, my research revolves around the following question for Sierra Nevada aquatic systems: how are aquatic invertebrate communities responding to abiotic changes in mountain watersheds (e.g. water temperature, phenological changes in snowmelt), and how are these responses mediated by landscape factors? Some of my focal study species include mayflies like Callibaetis ferrugineus and Ameletus edmundsii. 

#plotly graph
library(plotly)
library(tidyverse)
glimpse(storms)
## Observations: 10,010
## Variables: 13
## $ name        <chr> "Amy", "Amy", "Amy", "Amy", "Amy", "Amy", "Amy", "Am…
## $ year        <dbl> 1975, 1975, 1975, 1975, 1975, 1975, 1975, 1975, 1975…
## $ month       <dbl> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7…
## $ day         <int> 27, 27, 27, 27, 28, 28, 28, 28, 29, 29, 29, 29, 30, …
## $ hour        <dbl> 0, 6, 12, 18, 0, 6, 12, 18, 0, 6, 12, 18, 0, 6, 12, …
## $ lat         <dbl> 27.5, 28.5, 29.5, 30.5, 31.5, 32.4, 33.3, 34.0, 34.4…
## $ long        <dbl> -79.0, -79.0, -79.0, -79.0, -78.8, -78.7, -78.0, -77…
## $ status      <chr> "tropical depression", "tropical depression", "tropi…
## $ category    <ord> -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0,…
## $ wind        <int> 25, 25, 25, 25, 25, 25, 25, 30, 35, 40, 45, 50, 50, …
## $ pressure    <int> 1013, 1013, 1013, 1013, 1012, 1012, 1011, 1006, 1004…
## $ ts_diameter <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ hu_diameter <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
plotly::ggplotly(ggplot(data=storms)+
  geom_point(aes(x = wind, y = pressure, color = status)))
#htmlTable
table_means <- storms %>%
  drop_na(ts_diameter, hu_diameter) %>% 
  group_by(status) %>% 
  summarise(mean_wind = mean(wind), mean_pressure = mean(pressure), mean_ts = mean(ts_diameter), mean_hu = mean(hu_diameter))

table_means$mean_wind <- round(table_means$mean_wind, digits = 2)
table_means$mean_pressure <- round(table_means$mean_pressure, digits = 2)
table_means$mean_ts <- round(table_means$mean_ts, digits = 2)
table_means$mean_hu <- round(table_means$mean_hu, digits = 2)

htmlTable::htmlTable(table_means)
status mean_wind mean_pressure mean_ts mean_hu
1 hurricane 87.15 966.35 288.11 72.96
2 tropical depression 28.21 1006.47 0 0
3 tropical storm 45.75 999.03 159.61 0.04 
#Challenge
hurricane_length <- storms %>% 
  filter(status == "hurricane", year >= 2010) %>% 
  group_by(name, year) %>% 
  summarise(num_days = diff(range(day)))
  
map2_chr(.x = hurricane_length$name, .y = hurricane_length$num_days, function(x,y) paste("Hurricane", x, "lasted", y, "days"))
##  [1] "Hurricane Alex lasted 29 days"    
##  [2] "Hurricane Arthur lasted 2 days"   
##  [3] "Hurricane Chris lasted 0 days"    
##  [4] "Hurricane Cristobal lasted 3 days"
##  [5] "Hurricane Danielle lasted 7 days" 
##  [6] "Hurricane Danny lasted 2 days"    
##  [7] "Hurricane Edouard lasted 4 days"  
##  [8] "Hurricane Ernesto lasted 1 days"  
##  [9] "Hurricane Fay lasted 0 days"      
## [10] "Hurricane Fred lasted 30 days"    
## [11] "Hurricane Gonzalo lasted 6 days"  
## [12] "Hurricane Gordon lasted 2 days"   
## [13] "Hurricane Humberto lasted 2 days" 
## [14] "Hurricane Igor lasted 9 days"     
## [15] "Hurricane Ingrid lasted 2 days"   
## [16] "Hurricane Isaac lasted 1 days"    
## [17] "Hurricane Joaquin lasted 29 days" 
## [18] "Hurricane Julia lasted 3 days"    
## [19] "Hurricane Karl lasted 1 days"     
## [20] "Hurricane Kate lasted 0 days"     
## [21] "Hurricane Katia lasted 9 days"    
## [22] "Hurricane Kirk lasted 30 days"    
## [23] "Hurricane Leslie lasted 6 days"   
## [24] "Hurricane Lisa lasted 1 days"     
## [25] "Hurricane Maria lasted 1 days"    
## [26] "Hurricane Michael lasted 5 days"  
## [27] "Hurricane Nadine lasted 29 days"  
## [28] "Hurricane Nate lasted 1 days"     
## [29] "Hurricane Ophelia lasted 29 days" 
## [30] "Hurricane Otto lasted 1 days"     
## [31] "Hurricane Paula lasted 2 days"    
## [32] "Hurricane Philippe lasted 4 days" 
## [33] "Hurricane Rafael lasted 2 days"   
## [34] "Hurricane Richard lasted 1 days"  
## [35] "Hurricane Rina lasted 3 days"     
## [36] "Hurricane Sandy lasted 5 days"    
## [37] "Hurricane Shary lasted 0 days"    
## [38] "Hurricane Tomas lasted 26 days"